Integrated Analysis of Multiple Microarray Datasets Identifies a Reproducible Survival Predictor in Ovarian Cancer

BACKGROUND: Public data integration may help overcome challenges in clinical implementation of microarray profiles. We integrated several ovarian cancer datasets to identify a reproducible predictor of survival. METHODOLOGY/PRINCIPAL FINDINGS: Four microarray datasets from different institutions comprising 265 advanced stage tumors were uniformly reprocessed into a single training dataset, also adjusting for inter-laboratory variation ("batch-effect"). Supervised principal component survival analysis was employed to identify prognostic models. Models were independently validated in a 61-patient cohort using a custom array genechip and a publicly available 229-array dataset. Molecular correspondence of high- and low-risk outcome groups between training and validation datasets was demonstrated using Subclass Mapping. Previously established molecular phenotypes in the 2(nd) validation set were correlated with high and low-risk outcome groups. Functional representational and pathway analysis was used to explore gene networks associated with high and low risk phenotypes. A 19-gene model showed optimal performance in the training set (median OS 31 and 78 months, p < 0.01), 1(st) validation set (median OS 32 months versus not-yet-reached, p = 0.026) and 2(nd) validation set (median OS 43 versus 61 months, p = 0.013) maintaining independent prognostic power in multivariate analysis. There was strong molecular correspondence of the respective high- and low-risk tumors between training and 1(st) validation set. Low and high-risk tumors were enriched for favorable and unfavorable molecular subtypes and pathways, previously defined in the public 2(nd) validation set. CONCLUSIONS/SIGNIFICANCE: Integration of previously generated cancer microarray datasets may lead to robust and widely applicable survival predictors. These predictors are not simply a compilation of prognostic genes but appear to track true molecular phenotypes of good- and poor-outcome

Expression profiling identifies genes involved in neoplastic transformation of serous ovarian cancer

Background: The malignant potential of serous ovarian tumors, the most common ovarian tumor subtype, varies from benign to low malignant potential (LMP) tumors to frankly invasive cancers. Given the uncertainty about the relationship between these different forms, we compared their patterns of gene expression. Methods: Expression profiling was carried out on samples of 7 benign, 7 LMP and 28 invasive (moderate and poorly differentiated) serous tumors and four whole normal ovaries using oligonucleotide microarrays representing over 21,000 genes. Results: We identified 311 transcripts that distinguished invasive from benign tumors, and 20 transcripts that were significantly differentially expressed between invasive and LMP tumors at p < 0.01 (with multiple testing correction). Five genes that were differentially expressed between invasive and either benign or normal tissues were validated by real time PCR in an independent panel of 46 serous tumors (4 benign, 7 LMP, 35 invasive). Overexpression of SLPI and WNT7A and down-regulation of C6orf31, PDGFRA and GLTSCR2 were measured in invasive and LMP compared with benign and normal tissues. Over-expression of WNT7A in an ovarian cancer cell line led to increased migration and invasive capacity. Conclusion: These results highlight several genes that may play an important role across the spectrum of serous ovarian tumorigenesis

C-Fos expression is a molecular predictor of progression and survival in epithelial ovarian carcinoma

Members of the Fos protein family dimerise with Jun proteins to form the AP-1 transcription factor complex. They have a central function in proliferation and differentiation of normal tissue as well as in oncogenic transformation and tumour progression. We analysed the expression of c-Fos, FosB, Fra-1 and Fra-2 to investigate the function of Fos transcription factors in ovarian cancer. A total of 101 patients were included in the study. Expression of Fos proteins was determined by western blot analysis, quantified by densitometry and verified by immunohistochemistry. Reduced c-Fos expression was independently associated with unfavourable progression-free survival (20.6, 31.6 and 51.2 months for patients with low, moderate and high c-Fos expression; P=0.003) as well as overall survival (23.8, 46.0 and 55.5 months for low, moderate and high c-Fos levels; P=0.003). No correlations were observed for FosB, Fra-1 and Fra-2. We conclude that loss of c-Fos expression is associated with tumour progression in ovarian carcinoma and that c-Fos may be a prognostic factor. These results are in contrast to the classic concept of c-Fos as an oncogene, but are supported by the recently discovered tumour-suppressing and proapoptotic function of c-Fos in various cancer types

Clinical management of borderline tumours of the ovary: results of a multicentre survey of 323 clinics in Germany

The aim of this survey was to analyse the standard of care in diagnostic, surgery, chemotherapy and aftercare management for patients with borderline tumours of the ovary (BOTs) in Germany. A structured questionnaire comprising different dimensions was sent to all 1114 gynaecological departments. The questionnaire could be returned anonymously. The overall response rate was 29.0% (323 departments). Most departments were on secondary care (71.8%), tertiary care (23.2%) or university hospital (5.0%) level. Most clinicians performed not more than five BOT operations (89.2%) per year. Most departments (93.2%) used in addition to classical bimanual examination and vaginal ultrasound, tumour marker CA-125 detection, CT scan, MRI or PET-CT techniques. Departments in university and tertiary care hospitals performed more often a fresh frozen section (87 vs 64%). In young women, clinicians performed much seldom unilateral salpingo-oophorectomy (92%) and only in 53% biopsies of the contralateral ovary. Generally, biopsies of the contralateral ovary were performed in 4–53% of the patients. Chemotherapy was mostly favoured in ‘high-risk' patients with tumour residual, microinvasion or invasive implants. Thus, a high grade of insecurity in diagnostic and therapy of BOT exists in some gynaecological departments and underlines the need for more educational and study activities

Gene expression profiling of primary cultures of ovarian epithelial cells identifies novel molecular classifiers of ovarian cancer

In order to elucidate the biological variance between normal ovarian surface epithelial (NOSE) and epithelial ovarian cancer (EOC) cells, and to build a molecular classifier to discover new markers distinguishing these cells, we analysed gene expression patterns of 65 primary cultures of these tissues by oligonucleotide microarray. Unsupervised clustering highlights three subgroups of tumours: low malignant potential tumours, invasive solid tumours and tumour cells derived from ascites. We selected 18 genes with expression profiles that enable the distinction of NOSE from these three groups of EOC with 92% accuracy. Validation using an independent published data set derived from tissues or primary cultures confirmed a high accuracy (87–96%). The distinctive expression pattern of a subset of genes was validated by quantitative reverse transcription–PCR. An ovarian-specific tissue array representing tissues from NOSE and EOC samples of various subtypes and grades was used to further assess the protein expression patterns of two differentially expressed genes (Msln and BMP-2) by immunohistochemistry. This study highlights the relevance of using primary cultures of epithelial ovarian cells as a model system for gene profiling studies and demonstrates that the statistical analysis of gene expression profiling is a useful approach for selecting novel molecular tumour markers

Changes in Gene Expression and Cellular Architecture in an Ovarian Cancer Progression Model

BACKGROUND: Ovarian cancer is the fifth leading cause of cancer deaths among women. Early stage disease often remains undetected due the lack of symptoms and reliable biomarkers. The identification of early genetic changes could provide insights into novel signaling pathways that may be exploited for early detection and treatment. METHODOLOGY/PRINCIPAL FINDINGS: Mouse ovarian surface epithelial (MOSE) cells were used to identify stage-dependent changes in gene expression levels and signal transduction pathways by mouse whole genome microarray analyses and gene ontology. These cells have undergone spontaneous transformation in cell culture and transitioned from non-tumorigenic to intermediate and aggressive, malignant phenotypes. Significantly changed genes were overrepresented in a number of pathways, most notably the cytoskeleton functional category. Concurrent with gene expression changes, the cytoskeletal architecture became progressively disorganized, resulting in aberrant expression or subcellular distribution of key cytoskeletal regulatory proteins (focal adhesion kinase, α-actinin, and vinculin). The cytoskeletal disorganization was accompanied by altered patterns of serine and tyrosine phosphorylation as well as changed expression and subcellular localization of integral signaling intermediates APC and PKCβII. CONCLUSIONS/SIGNIFICANCE: Our studies have identified genes that are aberrantly expressed during MOSE cell neoplastic progression. We show that early stage dysregulation of actin microfilaments is followed by progressive disorganization of microtubules and intermediate filaments at later stages. These stage-specific, step-wise changes provide further insights into the time and spatial sequence of events that lead to the fully transformed state since these changes are also observed in aggressive human ovarian cancer cell lines independent of their histological type. Moreover, our studies support a link between aberrant cytoskeleton organization and regulation of important downstream signaling events that may be involved in cancer progression. Thus, our MOSE-derived cell model represents a unique model for in depth mechanistic studies of ovarian cancer progression

Identification of Tumor Suppressors and Oncogenes from Genomic and Epigenetic Features in Ovarian Cancer

The identification of genetic and epigenetic alterations from primary tumor cells has become a common method to identify genes critical to the development and progression of cancer. We seek to identify those genetic and epigenetic aberrations that have the most impact on gene function within the tumor. First, we perform a bioinformatic analysis of copy number variation (CNV) and DNA methylation covering the genetic landscape of ovarian cancer tumor cells. We separately examined CNV and DNA methylation for 42 primary serous ovarian cancer samples using MOMA-ROMA assays and 379 tumor samples analyzed by The Cancer Genome Atlas. We have identified 346 genes with significant deletions or amplifications among the tumor samples. Utilizing associated gene expression data we predict 156 genes with altered copy number and correlated changes in expression. Among these genes CCNE1, POP4, UQCRB, PHF20L1 and C19orf2 were identified within both data sets. We were specifically interested in copy number variation as our base genomic property in the prediction of tumor suppressors and oncogenes in the altered ovarian tumor. We therefore identify changes in DNA methylation and expression for all amplified and deleted genes. We statistically define tumor suppressor and oncogenic features for these modalities and perform a correlation analysis with expression. We predicted 611 potential oncogenes and tumor suppressors candidates by integrating these data types. Genes with a strong correlation for methylation dependent expression changes exhibited at varying copy number aberrations include CDCA8, ATAD2, CDKN2A, RAB25, AURKA, BOP1 and EIF2C3. We provide copy number variation and DNA methylation analysis for over 11,500 individual genes covering the genetic landscape of ovarian cancer tumors. We show the extent of genomic and epigenetic alterations for known tumor suppressors and oncogenes and also use these defined features to identify potential ovarian cancer gene candidates